Robustness of Multi Biometric Authentication Systems against Spoofing
Bibliographic record
Abstract
Nowadays biometric authentication systems have been more developed, especially in secure and financial systems; so cracking a biometric authentication system is now a growing concern. But their security has not received enough attention. Imitating a biometric trait of a genuine user to deceive a system, spoofing, is the most important attacking method. Multi biometric systems have been developed to overcome some weaknesses of single biometric systems because the forger needs to imitate more than one trait. No research has further investigated the vulnerability of multimodal systems against spoof attack. We empirically examine the robustness of five fixed rules combining similarity scores of face and fingerprint traits in a bimodal system. By producing different spoof scores, the robustness of fixed combination rules is examined against various possibilities of spoofing. Robustness of a multi biometric system depends on the combination rule, the spoof trait and the intensity of spoofing. Min rule shows the most robustness when face is spoofed especially in very secure systems but when the fingerprint is faked the max rule shows the least vulnerability against possibilities of spoofing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".